The Use of Genetic Markers to Determine Risk for Prostate Cancer at Prostate Biopsy
Bibliographic record
Abstract
PURPOSE: We examined a panel of 13 polymorphisms in 13 different genes to determine whether specific genotypes can help predict prostate cancer at the time of biopsy among men prescreened with prostate-specific antigen and digital rectal exam. EXPERIMENTAL DESIGN: We examined 2,088 consecutive men who were referred for prostate biopsy from 1997 to 2003. Thirteen genes were examined, including TNF308, GSTT1, KLK2, endostatin, MCRA, MCRV, tyrosinase, MSR1, CHK2, RNasel, HOGG1-326, HOGG1-11657, and HRAS1. Odds ratio for detection of prostate cancer were adjusted for age, race, prostate-specific antigen, digital rectal exam, family history of prostate cancer, and urinary symptoms. RESULTS: Of the 2,088 men, 996 (47.7%) had cancer detected. Four genes (TNF308, GSTT1, KLK2, and HOGG1-326) were significantly associated with prostate cancer. The adjusted odds ratios (OR) for prostate cancer for patients with the AA genotype of the TNF308 gene was 1.92 [95% confidence interval (95% CI), 1.0-1.5, P = 0.05], compared with those with the GG genotype, and for patients with the TT genotype of the KLK2 gene, the OR was 1.5 (95% confidence interval, 1.0-2.2, P = 0.04), compared with the CC genotype. The OR for patients with a homozygous deletion of the GSTT1 gene was 0.81 (95% CI, 0.6-1.0, P = 0.06) compared with those with the deletion, and the OR for patients with the GG genotype of the HOGG1-326 gene was 0.68 (95% CI, 0.5-1.0, P = 0.05) compared with the CC genotype. Patients who had all four alleles that were positively associated with prostate cancer had an OR of 9.33 (95% CI, 2.4-35.8, P = 0.0005) for prostate cancer compared with patients with alleles that were negatively associated with prostate cancer. CONCLUSIONS: Of the 13 polymorphisms, two were found to be positively associated with prostate cancer (TNF308 and KLK2) and two were negatively associated with prostate cancer (GSTT1 and HOGG1-326). Future studies are required to confirm these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".